Hallo everybody!

I am trying to install Agi4x44PreProcess on a Windows-mashine using R version 3.1.2 and Bioconductor version 3.0 (BiocInstaller 1.16.1).

As I understand Agi4x44 depends on R (>= 2.10), yet I am unable to install it.

Error message reads: "package ‘Agi4x44PreProcess’ is not available (for R version 3.1.2)"

Is there a work-around known to implement the functions of this package? Or is downgrading R to Version 2.10 the only option available?

Thank you in advance,

D.L. Lindenwald

Mr. MacDonald, thank you very much for the quick response.

The function CV.rep.probes (allows to access the reproducibility) is something i only found in the Agi4x44PreProcess package... Are other procedures to compute the coefficient of variation from non-control probes available?

I don't know of one, but it wouldn't be difficult to roll your own. Say we have an EList that we created using read.maimages(), and we called it dat.olf.

Great! Thank you, you are very helpfull!

Being a beginner I will need some time to fully understand what you did here :)

Edit_01: especially concerning the following string

zzz <- zz[sapply(zz, length) == 5]

It returns a named list with length of zero. Probably due to the fact that the line "sapply(zz, length) == 5" produces a matrix consisting of probe names and logical values "False"

Right. I am using a different array than you are, so I should have made that a bit more robust to different arrays. For my array, the duplicated (non-control) probes are all repeated five times each, so that makes sense for me. However, your array is obviously different. If you do

You will get a named vector, where the names correspond to the number of repeats, and the values correspond to the number of times a given number of repeats were observed. So for my array, I get

Which means that I have 14,356 probes that are only found one time on the array, and 50 probes that are found 5 times each. To make my code more portable, I could have done

Now I understand a bit more! Thank you alot for your time.

However the lapply-step cvs<-lapply(...) produced the error "dim(X) must have a positive length".I have looked for the source of the error and found out that the variable $E does not correspond to any data in my RG-List.

My intuitive approach was changing $E to $R for gProcessedSignal. This produces a data frame with my microarray and corresponding numerical value (median CV)

The confusing part is - my microarrays were printed multiple times with varying median CV's, batxches separated by strings like this:

structure.c.0.0840585083911646..0.0770087297194007..0.141343292808047..

can 1W G 0.08405851

can 1W K 0.07700873

can 2W G 0.14134329

can 2W K 0.13306410

can 3W G 0.25818046

can 3W K 0.18594718

cat 1W G 0.18594324

cat 1W K 0.09951136

cat 2W G 0.29593127

cat 2W K 0.30717095

cat 3W G 0.45109317

cat 3W K 0.09883565

Kon 1M G 0.04542627

Kon 1W K 0.34784651

Kon 2W G 0.14887225

Kon 2W K 0.04292610

Kon 3W G 0.29127059

Kon 3W K 0.08773394

structure.c.0.0256170315904253..0.0300330532944272..0.0228013445289736..

can 1W G 0.02561703

can 1W K 0.03003305

can 2W G

Was using $R wrong? Or does my result still make sense?